1. Field of the Invention
The present invention relates to decision support tools for air traffic control (ATC) and to simulation and modeling of air traffic.
2. Related Art
In modern ATC systems, operational personnel use various decision support tools (DSTs) for aircraft route planning and for keeping aircraft safely separated as they move from origin to destination. Many of these tools include a trajectory modeling function to predict the future positions and altitudes of aircraft. Examples of such DSTs in the United States include the Collaborative Routing Coordination Tools (CRCT), the Center-TRACON Automation System (CTAS), En Route Automation Modernization (ERAM), the Enhanced Traffic Management System (ETMS), and the User Request Evaluation Tool (URET). Some of these tools are in operational use, while others are currently being used as development platforms for future ATC capabilities.
CRCT is the prototype of a set of decision support capabilities to assist traffic managers in formulating flow management strategies. CRCT generates trajectories and uses them to predict sector counts (i.e., the number of aircraft that will occupy each ATC sector during a future time interval) and to determine which aircraft might penetrate a problematic block of airspace known as a “flow constrained area.” CTAS is a suite of decision support tools designed to assist ATC personnel in air traffic management. CTAS tools rely on trajectory modeling to schedule and sequence aircraft for efficient and conflict-free delivery to the terminal area.
ERAM is a program to replace the existing software and hardware at en route ATC centers with a more modern architecture. Under ERAM, trajectory modeling is needed to support flight data processing and flight plan preprocessing. Among other things, ETMS provides air traffic managers with a capability called “monitor/alert,” which predicts airport, fix, and sector counts for 15-minute intervals. URET is a tool to help en route controllers detect and resolve impending aircraft-aircraft and aircraft-airspace conflicts. Using flight plan and radar track data, URET builds a trajectory for each aircraft, and uses these trajectories to predict if any pair of aircraft will be in conflict within the next 20 minutes, or if an aircraft will come within a parameter distance of special-use airspace.
Uncertainty is an inherent part of any air traffic system. The positions and altitudes of aircraft are not measured with perfect accuracy. Furthermore, aircraft trajectories are subject to random variations due to weather, navigational error, wind prediction errors, and so forth. Therefore, a well-designed DST must be tolerant to uncertainty. This is accomplished in various ways. For example, in predicting aircraft-aircraft conflicts, URET protects a region around the nominal trajectory of each flight by defining a set of “conformance bounds”—imaginary containment bounds at a certain distance from the nominal trajectory, within which the actual flight track is assumed to reside. If an aircraft's radar track moves outside of the current conformance bounds, the trajectory for that flight is rebuilt. If the conformance bounds for two different flights overlap in space and time, URET may issue a conflict alert to the controller.
This is illustrated in FIG. 1, in which the nominal trajectories of two aircraft are represented by 102A and 102B. The dashed lines 104A and 104B represent the lateral conformance bounds for the trajectories. Note that there are also vertical conformance bounds, not shown in the figure. Region 108, where the conformance bounds overlap, is where the two aircraft might generate an alert. The ideal span of URET's conformance bounds is a tradeoff between the need to keep aircraft safely separated and the need to use limited airspace efficiently. In principle, the conformance bounds could be adjusted according to current conditions (navigational equipment in use, planned maneuvers, etc.) to provide just the right amount of protection at any point along a route. However, parameters for controlling the size of such conformance bounds must be optimized by extensive testing with recorded and/or simulated air traffic.
In addition to the decision support tools listed above, a number of simulation and modeling tools (SMTs) have been developed over the years to model air traffic, as well as elements of the ATC system, in selected regions of airspace. These tools are used to evaluate and refine DSTs, to support airspace redesign, and to predict the effects of proposed changes to the ATC system on system performance. Examples of such tools include the National Airspace System Performance Analysis Capability (NASPAC), the Sector Design and Analysis Tool (SDAT), the Reorganised Mathematical ATC Simulator (RAMS), the Total Airspace and Airport Modeller (TAAM), and the Detailed Policy Assessment Tool (DPAT). Generally, SMTs model aircraft flights either by using a trajectory modeler to synthesize trajectories, or by “replaying” actual recorded tracks.
A desirable capability for an SMT is the ability to model uncertainty in aircraft positions and altitudes. For example, NASPAC can model such uncertainty to a degree by replacing nominal predicted trajectories (produced by a trajectory modeler) with actual recorded tracks for the same origins and destinations, selected randomly from a limited data base of such tracks (usually recorded on a single day). With this scheme, a certain amount of variation can be modeled, especially for city pairs for which there is a high level of air traffic. However, an extremely large data base of tracks would be required to assure representative variations over a wide range of weather conditions and for less heavily traveled routes.
In developing and testing DSTs, and in using SMTs effectively, the choice of a method for modeling air traffic often comes down to the replaying of recorded tracks vs. the synthesis of aircraft trajectories by a trajectory modeler. As mentioned in the NASPAC example above, the use of recorded tracks can allow uncertainty to be modeled to a limited extent. A high level of confidence in the results generally requires many computer runs with different sets (days) of recorded traffic data. In addition, the use of recorded tracks has a major limitation that is especially significant for the analysis of aircraft-aircraft and aircraft-airspace conflicts: in the recorded traffic data, conflicts are virtually always resolved by controller intervention. Hence, almost no recorded conflicts exhibit an actual violation of separation rules. Therefore, it becomes difficult to estimate what the outcome of a conflict would have been (for example, the minimum separation between two aircraft) if no outside intervention had occurred. This is not a problem with simulated trajectories, in which the a priori outcome is known accurately (by construction). However, simulated trajectories have a limitation of their own: they normally do not exhibit variations that are typical of the real world. This is because trajectory modelers are generally deterministic in nature; that is, given a specific set of initial conditions, the modeler will always produce the same result. Ideally, a trajectory modeler should be capable of simulating random variations that are typical of real aircraft trajectories. It is in this regard that the present invention fills a void.